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Research Award Winners Ravi Pandey and Shuanglin Zhang

Pandey Finds a New Way to Sequence DNA

By Marcia Goodrich

Ravi Pandey was trying to determine if nanotubes would work as taxis to deliver chemotherapy drugs to tumors. Then he discovered something quirky about DNA that could revolutionize gene-sequencing technology.

Chemotherapy is a tried-and-true cancer therapy, but for many patients, the drugs are so toxic that the cure is worse than the disease. So, rather than dosing the entire person with healing poisons, scientists want to shuttle those drugs directly to the tumor, with carbon nanotubes serving as the shuttle.

First, however, they want to make sure they aren't making things worse. Nanotubes are, as their name suggests, incredibly tiny, not much bigger than a strand of DNA.

Common prudence would suggest that, before injecting them into people, you would want to make sure they don't cause more problems than they solve.

Pandey, chair of Tech's physics department, and his team wanted to find out if carbon nanotubes react with the bases of DNA—adenine, cytosine, guanine, and thymine, or ACGT for short. If those bases are not reactive, then carbon nanotubes are probably safe.

So, they went down to the sub-molecular level, to the clouds of electrons that hover over atoms and molecules. When molecules and atoms get close to each other, they deform that electron cloud. Some deform more than others, a quality known as polarizability.

If the polarizability is really significant, substances tend to bind strongly to each other. When Pandey's group calculated the polarizability of A, C, G, and T vis-a-vis carbon nanotubes, however, he got good news: low binding, meaning that carbon nanotubes had crossed one bridge on the path to a better way to deliver chemotherapy drugs.

But Pandey noticed something else. With respect to the carbon nanotube, they found slight differences in each of DNA's four bases.

"They are subtle, but there are differences in the binding energy that come from polarizability," says Pandey. "At one of our conferences, we sat down at dinner one evening and asked, ‘Could we apply these differences somehow?'"

The answer was an emphatic "maybe." Maybe you could sequence DNA by somehow measuring the binding energy of each of the ACGT bases, one after another. "It was a little hunch, a napkin decision," he says.

Back at the University, Pandey and his team began to turn the back-of-the-napkin maybe into a yes, with collaboration with Trinity College, the Army Research Lab, and Uppsala University.

Using computer modeling, they developed a new way to sequence DNA that could be far easier and cheaper than current methods.

"You just pull strands of DNA through a carbon nanotube membrane with an electric current going through it," Pandey says. It's a little more complicated than that, but tiny changes in the voltage signal which base is which, in perfect order, along the famous double helix.

Present sequencing methods are expensive and slow, and Pandey hopes that their breakthrough might someday revolutionize the technology.

"This is only possible because the scale of materials has gone down to the nano-level,"

says Pandey. "We're using quantum mechanics to understand biological processes. It's the fusion of biology and physics—a whole new world."

Zhang Studies Genetic Links to Diseases

By Marcia Goodrich

A team of Michigan Tech mathematicians led by Professor Shuanglin Zhang, who was recently awarded the Richard and Elizabeth Henes Professorship in Mathematical Sciences, has developed powerful new tools for winnowing out the genes linked to some of humanity's most intractable diseases.

With one, they can cast back through generations to pinpoint the genes behind inherited illness. With another, they have isolated eleven genes associated with type-2 diabetes. The team spokesperson is Qiuying Sha, Zhang's wife and an assistant professor of mathematical sciences. Zhang has contracted another genetically driven condition: amyotrophic lateral sclerosis (ALS), also known as Lou Gehrig's disease, which has made speaking difficult. Ironically, his work may one day pave the way to a cure. "With chronic, complex diseases like Parkinson's, diabetes, and ALS, multiple genes are involved," said Sha. "It is critical to develop statistical methods that can account for gene-gene interactions and can analyze these genes jointly."

This team has developed the Ensemble Learning Approach (ELA), software used to detect a set of genes that together have a significant effect on a disease.

With complex inherited conditions, including type-2 diabetes, single genes may precipitate the disease on their own, while other genes cause disease when they act together.

In the past, finding these gene-gene combinations has been especially unwieldy because the calculations needed to match up suspect markers among the five-hundred thousand or so in the human genome have been virtually impossible. ELA sidesteps this problem, first by drastically narrowing the field of potentially dangerous genes, and second, by determining which genetic variants act on their own and which act in combination. "We thought it will be a powerful tool to help finding disease-related genes for complex diseases," Sha said.

ELA is also used to compare the genetic makeup of unrelated individuals to sort out disease-related genes. The team has also developed another approach, which uses a two-stage association test that incorporates founders' phenotypes, called TTFP, that can examine the genomes of family members going back generations.

"In the past, researchers have dealt with the nuclear family, parents, and children, but this could go back to grandparents, great-grandparents . . . as far back as you want." The team has published their findings in the European Journal of Human Genetics. An abstract is available at www.nature.com/ejhg/journal/v15/n11/abs/5201902a.html. Other members of Michigan Tech's statistical genetics group are postdoctoral scientists Zhaogong Zhang and Tao Feng.

Now that they've developed the software, the analysis is relatively simple, says Sha. But getting the genetic data to work on is not. "We don't have the data sets yet to work with," she says, clearly frustrated.

Those who do have data sets, however, can use the team's software to help find the cause—and hopefully, the cures—for a multitude of illnesses. Maybe even Lou Gehrig's disease.